Abstract
The aim of this work is to provide a review of the main indoor positioning methodologies, in order to evidence their strengths and weaknesses, and explore the potential of the integration in an Unmanned Ground Vehicle built for tunnel monitoring purposes. A robotic platform, named Bulldog, has been designed and assembled by Sipal S.p.a., with the support of the research group Applied Geomatic laboratory (AGlab) of the Politecnico di Bari, in the definition of the data processing pipeline. Preliminary results show that the integration of indoor positioning techniques in the Bulldog platform represents an important advance for accurate monitoring and analysis of a tunnel during the construction stage, allowing a fast and reliable survey of the indoor environment and requiring, at this prototypal stage of development, only a remote supervision by the operator. Expected improvements will allow to carry out tunnel monitoring activities in a fully autonomous mode, bringing benefit for the safety of people involved in the construction works and the accuracy of the acquired dataset.
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References
Mendoza Silva, G., Torres-Sospedra, J., Huerta, J.: A Meta-Review of Indoor Positioning Systems. Sensors 19(20), 4507 (2019)
Rosinol Vidal, A., Rebecq, H., Horstschaefer, T., Scaramuzza, D.: Ultimate SLAM? combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robot. Autom. Lett. 3(2), 994–1001 (2018)
Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)
Mascitelli, A.: An open source platform for indoor navigation: application to the Faculty of Civil and Industrial Engineering of Sapienza, University of Rome. Bollettino Sifet n.2- Sezione Scienza (2017)
Schneider, O.: Requirements for positioning and navigation in underground constructions. In: Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 15–17 September, Campus Science City, ETH Zurich, Switzerland (2010)
Mautz, R.: Indoor Positioning Technologies. In: Sechsundachtzigster Band +, vol. 86 (2012)
Ravanelli, R., Nascetti, A., Crespi, M.: Kinect V2 and RGB stereo-cameras integration for depth map enhancement. Int. Arch. Photogrammetry Remote Sens. Spatial Inform. Sci. XLI-B5–XXIII, 699–702 (2016)
Duan, C., Junginger, S., Huang, J., Jin, K., Thurow, K.: Deep learning for visual SLAM in transportation robotics: a review. Transp. Safe. Environ. 1, 177–184 (2019)
Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127–136. IEEE (2011)
Izadi, S., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011)
Whelan, T., Kaess, M., Fallon, M., Johannsson, H., Leonard, J. J., McDonald, J.: Kintinuous: Spatially extended kinectfusion. Technical report, MIT CSAIL (2012)
Sakpere, W., Adeyeye-Oshin, M., Mlitwa, N.B.W.: A state-of-the-art survey of indoor positioning and navigation systems and technologies. S. Afr. Comput. J. 29(3), 145–197 (2017)
Priyantha, N.B.: The cricket indoor location system. Doctoral dissertation, Massachusetts Institute of Technology (2005)
Yassin, A., et al.: Recent advances in indoor localization: a survey on theoretical approaches and applications. IEEE Commun. Surv. Tutor. 2017(19), 1327–1346 (2017)
Schmidt, E., Huang, Y., Akopian, D.: Indoor positioning via WLAN channel state information and machine learning classification approaches. In: Proceedings of ION GNSS+, pp. 355–166 (2019)
Bahl, P., Padmanabhan, V.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784 (2000)
Youssef, M., Agrawala, A.: He Horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services (MobiSys 2005), Seattle, WA, USA, pp. 205–218 (2005)
Schmidt, E., Akopian, D.: Fast prototyping of an SDR WLAN 802.11b Receiver for an indoor positioning systems. In: Proceedings of 31st International Technical Meeting Satellite Division Institute Navigation (ION GNSS), Miami, FL, USA, pp. 674–684 (2018)
Wang, X., Gao, L., Mao, S., Pandey, S.: CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans. Veh. Technol. 66(1), 763–776 (2017)
Wang, X., Gao, L., Mao, S.: CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J. 3(6), 1113–1123 (2016)
Hsieh, C.-H., Chen, J.-Y., Nien, B.-H: Deep learning-based indoor localization using received signal strength and channel state information. IEEE Access 7, 33256–33267 (2019)
Chen, H., Zhang, Y., Li, W., Tao, X., Zhang, P.: ConFi: convolutional neural networks based indoor Wi-Fi localization using channel state information. IEEE Access 5, 18066–18074 (2017)
Brena, R.F.; García-Vázquez, J.P.; Galván-Tejada, C.E.; Muñoz-Rodriguez, D.; Vargas-Rosales, C.; Fangmeyer, J.: Evolution of indoor positioning technologies: a survey. J. Sens. 2017, 1–21 (2017)
Gabela, J., et al.: Experimental evaluation of a UWB-based cooperative positioning system for pedestrians in GNSS-denied environment. Sensors 2019, 5274 (2019)
Mazhar, F., Khan, M.G., Sällberg, B.: Precise indoor positioning using UWB: a review of methods, algorithms and implementations. Wireless Pers. Commun. 97(3), 4467–4491 (2017). https://doi.org/10.1007/s11277-017-4734-x
Xu, B., Sun, G., Yu, R., Yang, Z.: High-accuracy TDOA-based localization without time synchronization. IEEE Trans. Parallel Distrib. Syst. 24(8), 1567–1576 (2013)
Pittet, S., Renaudin, V., Merminod, B., Kasser, M.: UWB and MEMS-based indoor navigation. J. Navig. 61, 369–384 (2008)
Zhang, J., Li, B., Dempster, A.G., Rizos, C.: Evaluation of high sensitivity GPS receivers. In: International Symposium on GPS/GNSS Taipei, Taiwan. 26–28 October 2010
Xu, R., Chen, W., Xu, Y., Ji, S.: A new indoor positioning system architecture using GPS signals. Sensors 15, 10074–10087 (2015)
D’Aranno, P.J.V.: High-resolution geomatic and geophysical techniques integrated with chemical analyses for the characterization of a Roman wall. J. Cult. Heritage 17, 141–150 (2016)
Argese, F., et al.: Piattaforma HW/SW per la gestione dei Cantieri Tecnologici per Infrastrutture Civili. Atti Asita (2019)
ROS Website. http://www.ros.org. Accessed 15 Feb 2020
Turtlebot Website. http://www.turtlebot.com. Accessed 15 Feb 2020
Acknowledgements
This research is funded by the project “Technological Construction Site for Military and Civil Infrastructures/Cantiere Tecnologico per Infrastrutture Militari e Civili.” (Unmanned Vehicles and Virtual Facilities), co-financed by the European Union-European Regional Development Fund POR Puglia 2014/2020 and Puglia Region.
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Sonnessa, A., Saponaro, M., Alfio, V.S., Capolupo, A., Turso, A., Tarantino, E. (2020). Indoor Positioning Methods – A Short Review and First Tests Using a Robotic Platform for Tunnel Monitoring. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_48
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